However, AUC is influenced by the base PCC of a model, making it difficult to compare the rejection performance.

a metric independent of predictive performance is needed.


By default, we use a beam size of 5 and take the first hypothesis, but we also observe that higher beam size and lower-ranked hypotheses can generate good simplification results.

we generate the first two candidate hypotheses for each beam size from 5 to 12.


It is one of the largest freely available resources for text simplification, and unlike the previously used EW-SEW corpus 2 (Kauchak, 2013), which only contains full matches (167K pairs), the newer dataset also contains partial matches.

it is not only larger, but it also allows for learning sentence shortening (dropping irrelevant parts) transformations (see Table 3  We use the Stanford NER system (Finkel et al., 2005) to get an approximate number of locations, persons, organizations and miscellaneous entities in the corpus.


At each step, only one or two words are affected, with very local attachments made.

distant attachments require long and not immediately obvious transition sequences (e.g., ate→chopsticks in Figure 1, which requires two transitions).


Arc-eager (Nivre, 2003) remedies this drawback by defining arc-inducing transitions that operate between the stack and the buffer.

a constituent no longer needs to be complete before it can be attached to its head to the left, as a right arc doesn't prevent the attached dependent from taking further dependents of its own.


Then the model can be defined as: alignment model (3) Our feed-forward alignment model has the same architecture ( Figure 1) as the one proposed in (Alkhouli et al., 2016).

the alignment probability can be modeled by: and m indicates the window size.


∆ i = b i − b i−1 denotes the jump from the predecessor position to the current position.

the jump over the source is estimated based on a m-words source context window and n predecessor target words.


However since it was trained on movie reviews, it is likely to be missing labelled data for some common phrases in our blogs.

we also retrained it (RETRAINED STANFORD) on high precision phrases from AutoSlog extracted from our training data of positive and negative blogs.


But those aspects that appear in multiple past domains are more likely to be correct.

k contains those frequent aspects in S. The lifelong extraction phase is in Algorithm 1.


But the gradients can actually be back-propagated in a distributed way for each channel, since the convolutional and embedding layers are independent across these.

training time scales approximately linearly with the number of parameters in one channel (if the gradient is back-propagated in a distributed way).


These models are based on a relatively simple log-linear method and avoid hidden layers typical to neural networks.

they can be trained to produce high-quality word embeddings on large corpora like the entirety of English Wikipedia in several hours, compared to days or even weeks in the case of other continuous models.


The number of summed-over elements in the third term of (8), however, is quadratic in the size of the vocabulary, making it hard to compute.

in practice, we can approximate the expectation by sampling of 'negative' examples.


Theorem 2: The difference between the SGNS score at the PMI matrix and the SGNS score at a given matrix m can be written as: The KL divergence between two distributions is always non-negative and is zero only if the two distributions are the same.

we rederive the results of (Levy and Goldberg, 2014) that S(pmi) = max m S(m).


The limited training data will cause unbalanced actions and lead to the problems of data sparsity and overfitting.

we propose to use the transition-based model to parse a naked dis-course tree (i.e., identifying span and nuclearity) in the first stage.


These new actions do not add an arc between tokens but instead they edit a single token.

the parser is able to jointly parse and correct grammatical errors in the input sentence.


(2014) show that the accuracy of distinguishing connectives is more than 97%, while identifying arguments is below than 80%.

we use the existing toolkit 1 to find discourse connectives, and just collect explicit instances using patterns like [arg1 because arg2], where two arguments are in the same sentence, to decrease noise.


Since compressive summarization methods can use a sub-sentence as an atomic unit, they can pack more information into summaries than extractive methods, which employ sentences as atomic units.

compressive summarization is essential when we want to produce summaries under tight length constraints.


As a result, researchers cannot know how much room for further improvement is left.

it is beneficial to reveal the upper bound summary that achieves the maximum ROUGE score and can be produced by the systems.


N (g n j , R k ) and N (g n j , S) are the number of occurrences of n-gram g n j in R k and S, respectively.

compressive oracle summaries are defined as follows: (2) T is the set of all valid word subsequences 1 obtained from sentences contained in the input document(s), and L max is the length limitation of the oracle summary.


Word subsequences, w 1,6 ,w 2,5 ,w 2,6 and w 3.9 match "live in".


The results demonstrated that compressive summarization is a promising approach to produce more informative summaries, and room still exists for further improvement.

compressive summarization is important research topic to leverage our resources.


Kanji is an ideogram and is more informative than the other types of Japanese letters.

if a noun in H and a verb in S 2 share one kanji, the noun and the verb are regarded as shared.


Logogram character features (char) Chinese and Japanese logograms are compositions of one or more characters defining their meanings.

different words representing the same event often contain similar characters.


However, they are oblivious to the presence of numbers in arguments.

they may miss important extractions and may not always output the best numerical facts.


2(b) are very similar to digit 1 which is very different from digit 0 in shape.

there is no activation in group 10 in Fig.


Following sequential CNNs, one dimensional convolutions operate the convolution kernel in sequential order where x i ∈ R e represents the e dimensional word representation for the i-th word in the sentence, and ⊕ is the concatenation operator.

x i,j refers to concatenated word vector from the i-th word to the (i + j)-th word in sentence.


While scheduled sampling has been empirically successful, its training objective has a drawback: because the procedure directly incorporates greedy decisions at each time step, the objective is discontinuous at parameter settings where previous decisions change their value.

gradients near these points are non-informative and scheduled sampling has difficulty assigning credit for errors.


Here, we propose a novel approach based on scheduled sampling that uses a differentiable approximation of previous greedy decoding decisions inside the training objective by incorporating a continuous relaxation of argmax.

our end-to-end relaxed greedy training objective is differentiable everywhere and fully continuous.


In such case, the output of T lies between zero and one.

when the transform gate is one, the networks pass through the transformation f over y l−1 and block the pass of input y l−1 ; when the gate is zero, the networks pass through the unmodified y l−1 , while the transformation f over y l−1 is suppressed.


That is, Equation 2 becomes: where [...;...] denotes concatenation.

at each layer l, the gate T l can regulate either passing through y l−1 to form y l , or retrieving novel knowledge from the input text x to augment y l−1 to create a better representation for y l .


To compute the similarity between arbitrary sentence pairs, we represent each sentence by averaging the embeddings of its constituent words.

3 of this first step, our method keeps for each source sentence the n closest target sentences (n being small, for instance with a value of 100) according to the similarity score.


The computation of this score can be highly costly, depending on the sentence length and the number of dimensions of the word embeddings.

we compute this score only for the source to target direction, unlike Kajiwara and Komachi (2016).


One challenging aspect of dealing with grammatical errors is that the errors usually do not stand on their own, but are dependent on the context (Chollampatt et al., 2016).

we need a model that considers the sequence of words in a sentence as a whole to determine which position needs correction.


However, the translated captions may be literal and unnatural because image information cannot be reflected in the translation.

in this study, we construct a Japanese image caption dataset, and for given images, we aim to generate more natural Japanese captions than translating the generated English captions into the Japanese ones.


For each image, we provided five Japanese captions.

the total number of captions was 820,310.


In our experiment, we compared the performance of Japanese caption generation by a neural network-based model with and without STAIR Captions to highlight the necessity of Japanese captions.

we showed the necessity of STAIR Captions.


In Penn Treebank, that is, an MWE-span does not always correspond to a span dominated by a single non-terminal node.

word-based dependency structures converted from Penn Treebank are generally inconsistent with MWE-spans ( Figure 1a).


By adding MWE-specific features to the joint model, however, we observe at least a 2.52 / 3.00 point improvement in terms of UAS / LAS regarding the first tokens of MWEs, and a 2.90 / 2.99 point improvement regarding FUM / FTM.

we obtain a 1.35 / 1.28 point improvement with joint(+pred span) compared with the pipeline model in terms of FUM / FTM.


For each relation, we generate a question per word pair consisting of the word pair plus 10 random word pairs from the same relation.

each question would consist of 11 word pairs (a i , b i ) where 1 ≤ i ≤ 11.


This is also evident in the case of the extrinsic evaluation.

when training word embeddings, it is crucial to use large training data to obtain meaningful embeddings.


This is mainly attributed to the fact that for Arabic, and unlike other languages such as English, different forms of the same word exist and these must be taken into consideration when evaluating the embeddings.

it is advised to use the top-k matches to perform the evaluation, where k is 5 for instance.


The annotations for an item from multiple respondents are usually averaged to obtain a real-valued score for that item.

for an N -item set, if each item is to be annotated by five respondents, then the number of annotations required is 5N .


2 A useful measure of quality is reproducibilityif repeated independent manual annotations from multiple respondents result in similar sentiment scores, then one can be confident that the scores capture the true sentiment intensities.

we set up an experiment that compares BWS and RS in terms of how similar the results are on repeated independent annotations.


7 Each 4-tuple was annotated by 10 workers.

the total number of annotations obtained for BWS was 20N (just as in RS).


σ = 1.17 compared to σ = 0.81 for the full set).

it seems that the outcomes of rating scale and BWS diverge to a greater extent when the complexity of the items to be rated increases.


are the true label and prediction probabilities for task A, N denotes the total number of training samples and C (A) is the total number of classes associated with the task A.

the parameters of our network are optimized for global objective function given by: where X={Age, Location, Political Orientation, Gender} We agglomerated data based on user tweets and their profile description.


Different from the previous study that modeled the difficulties of instances and the user's authority (Whitehill et al., 2009), we target at integrating multiple labels for each instance by estimating the matching degree of documents and annotators.

our work is applicable to aggregating individual sentiment labels in social media, where users under various scenarios (e.g., character and preference) may express invalid or noisy sentiments to different topics.


According to (Ustinovskiy et al., 2016), each target instance except for those in I F is annotated by three users.

the number of virtual users is set to 2oN .


In (Liu et al., 2016), current popular metrics are shown to be not well correlated with human judgements.


This allowed us to select the best sentences to be annotated by humans (sam-pleJS).


Once the attention is defined, the representation of the input is constructed as The problem with this traditional attention model is that the final hidden state is a function of all the inputs, hence it is usually more "informative" than the earlier hidden states due to semantic accumulation (Wang et al., 2016).

most of the attention signal is assigned to the hidden states toward the end of a sequence.


Furthermore, DA classification itself can be seen as a preprocessing step in a dialogue system's pipeline.

we also plan to investigate the effect of improvements in DA classification on the downstream components of a dialogue system.


In LDA each word w of a document d is assumed to be generated by first sampling a topic t ∈ T (where T is a set of K topics) from d's topic distribution θ d and then sampling a word from the distribution over words φ t of topic t. Both θ d and φ t are drawn from conjugate Dirichlet priors α and β, respectively.

the probability of word w, given document d and the priors α and β, is for t ∈ T , where p(t | w) is estimated via LDA.


In order to achieve performance and configurability, the Salience Rank (SR) algorithm combines the K latent topics estimated by LDA into a word metric, called word salience, and uses it as a preference value for each w i ∈ W .

sR needs to perform only a single run of PageRank on the word graph G in order to obtain a ranking of the words in each document.


We consider such mentions that we are confident to link as seed mentions, and use them to construct contextual and non-contextual information of the target entities to enhance entity disambiguation.

in this work, we propose to tackle the problem of List-only Entity Linking through seed mentions.


We are more confident to link "University of Pennsylvania" in d I and "Penn" in d II to University of Pennsylvania because other entities in the University list, such as "Harvard" and "MIT," also appear in the same document.

we select mentions in d I and d II as seed mentions.


We manually map each name to its pertinent Wikipedia page as a target entity (e.g., Starbucks → enwiki:Starbucks 3 ).

we collect lists containing 139 target entities in total.


In this work, without explicit relations between entities in different lists, we only take the cooccurrence of mentions in the same list into consideration.

our method is unable to benefit from the co-occurrence of John Lennon and Give Peace a Chance although they are actually strongly connected.


, n − 1, or, in other words, if it can be written in the form for some γ ∈ C n/2 −1 and ξ 0 , ξ n/2 ∈ R. The DFT F(x) is conjugate symmetric if and only if x is a real vector.

maintaining conjugate symmetry of "frequency" vectors is the key to ensure their "time" counterparts remain in real space.


We choose instead to focus on a subset of the vocabulary that we know to be poorly modeled by our baseline NMT system, namely words that occur rarely in the parallel corpus.

the goal of our data augmentation technique is to provide novel contexts for rare words.


The word j = arg max k P (y = k|h i ) will have the largest value of h T i w j + b j .

the arg max problem can be converted to finding the nearest neighbor of vector [h i ; 1] among the vectors [w j ; b j ] under the distance measure of dot-product.


The word alignment model, a foundation of phrase-base machine translation, also follows the same spirit in its generative story: each source word is translated to zero, one, or more target words and then reordered to form target sentences.

we apply the following algorithm to reduce the run-time vocabulary size: 1.


Unlike LSH, these table lookups and list mergings are performed once per sentence, and do not depend on the any hidden vectors.

we can overlap the computation with source side forward propagation.


those having fewer distinct senses shared by more translations of the same sentence).

we obtain a full, high-coverage version of EUROSENSE where each disambiguated word or multi-word expression (disambiguated instance) is associated with a coherence score.


Finally, in order to discard less confident annotations, we consider the cosine value associated with each refined disambiguation as confidence score, and use it to compare each disambiguated instance against an empirically validated threshold of 0.75.

we obtain the refined high-precision version of EUROSENSE, where each disambiguated instance is associated with both a coherence score and a distributional similarity score.


Word segmentation plays a pivotal role in improving any Arabic NLP application.

a lot of research has been spent in improving its accuracy.


As illustrated in Figure 2, it consists of a single encoder and a single decoder, which are used to generate outputs for all domains.

all model parameters are shared across domains, and the model is trained from all examples.


Language-based models to recognize counterfactual thinking in social media would potentially allow for psychological analysis on users based on their everyday language, avoiding the high expense of capturing counterfactual thinking at a large scale using traditional psychological assessments.

in this paper, we build a languagebased model to recognize counterfactual forms in social media texts of Twitter and Facebook.


For example, depending on the context, a Turkish word adam can be analyzed as: (i) a man - [ (Hakkani-Tür et al., 2002).

if one counts analyses as tags, MD can be cast as a tagging problem with an extremely large tagset.


Since each language has its own characteristics, the source and target languages should be encoded and learned independently.

we conjecture that the shared encoder may be a factor limit-ing the potential translation performance.


By reversing the translation direction of X ⇒ Y , our method can be used to train another two translation models p(z|y) and p(x|z).

the four translation models (p(z|x), p(x|z), p(z|y) and p(y|z)) of the rare language Z can be optimized jointly with our proposed unified bidirectional EM algorithm.


Nevertheless, in our settings, BLEU scores cannot be calculated during training due to the absence of the golden targets (z is generated based on x or y from the richresource pair (x, y)).

we choose IBM model1 scores to weight the generated translation candidates, with the word translation probabilities calculated based on the given bilingual data (the low-resource pair (x, z) or (y, z)).


To do back translation in two directions (e.g., X⇒Z and Z⇒X), we need monolingual data from both sides (e.g., X and Z), however, in TA-NMT, the monolingual data of Z is not necessary.

in the translation of X⇒Z or Y⇒Z, Back-Trans uses additional monolingual data of Z while TA-NMT does not, that is why BackTrans outperforms TA-NMT in these directions.


BPE and the unigram language model share the same idea that they encode a text using fewer bits with a certain data compression principle (dictionary vs. entropy).

we expect to see the same benefit as BPE with the unigram language model.


Our collection process focuses on precision.

the final set is diverse but not comprehensive, making evaluation non-trivial (see Section 5).


Similar constraints are enforced on entity vectors for additive models as well.

the average entity vector lengths are not affected by increasing vector dimension for all additive models.


They use a hierarchical attention model where the first level decoder predicts salient sentences used for an extractive summary and based on this output, the second step predicts keywords which are used for abstractive summarization.

they do not use key words for extractive summarization and for abstractive summarization they generate key words based on sentences predicted independently of key words.


We assume that similar sentences should hold similar summary patterns.

given a sentence x, we find out its analogies in the corpus and pick their summaries as the candidate templates.


However, r usually contains a lot of named entities that does not appear in the source (see Table 5).

it is hard to ensure that the soft templates are faithful to the input sentences.


For Rerank, we expect the predicted saliency s(r, x) close to the actual saliency s * (r, y * ).

where θ stands for the model parameters.


(2016) found that a large proportion of the words in the summary were copied from the source text.

they proposed CopyNet which considered the copying mechanism during generation.


Narayan and Gardent (2014) argued that syntactic structures do not always capture the semantic arguments of a frame, which may result in wrong splitting boundaries.

they proposed a supervised system (HYBRID) that uses semantic structures (Discourse Semantic Representations, (Kamp, 1981)) for sentence splitting and deletion.


Several influential factor analysis studies have shown that the three most important, largely independent, dimensions of word meaning are valence (positiveness-negativeness/pleasuredispleasure), arousal (active-passive), and dominance (dominant-submissive) (Osgood et al., 1957;Russell, 1980Russell, , 2003.

1 when comparing the meanings of two words, we can compare their degrees of valence, arousal, or domi-nance.


These factors are known to impact different groups of people differently (men vs. women, young vs. old, etc.).

it is not surprising that our understanding of the world may be slightly different de-pending on our demographic attributes.


(5a) would be annotated ORGROLE;LOCUS and (5b) as ORGROLE;BENEFICIARY to expose their common truth-semantic meaning but slightly different portrayals owing to the different prepositions.


However, the Gaussian distribution has unbounded support, namely R = (−∞, ∞), which does not satisfy the property of absolute bounds for appropriate scalar annotation (i.e., ratio scale in the level of measurement).

we propose a variant of TrueSkill by changing the latent distribution from a Gaussian to a beta, using a heuristic algorithm based on TrueSkill for inference.


We believe that developing effective argument generation models will enable a broad range of compelling applications, including debate coaching, improving students' essay writing skills, and pro- viding context of controversial issues from different perspectives.

there exists a pressing need for automating the argument construction process.


For a given statement, we construct one query per sentence to broaden the diversity of retrieved articles.

multiple passes of retrieval will be conducted if more than one query is created.


A binary distinction between fake and real news turned out to be infeasible, since hardly any piece of fake news is entirely false, and pieces of real news may not be flawless.

posts were rated "mostly true," "mixture of true and false," "mostly false," or, if the post was opinion-driven or otherwise lacked a factual claim, "no factual content."


In particular, we follow the notion that a counterargument attacks either the conclusion of an argument, the argument's premises, or both.

we compute two word and two embedding similarities as specified above for each candidate counterargument; once to the argument's conclusion (called w c and e c for words and embeddings respectively) and once to the argument's premises (w p and e p ).


Our goal is to investigate the effect of contextual features such as the length of context, word order and more, on LSTM performance.

we use ablation analysis, during evaluation, to measure changes in model performance in the absence of certain contextual information.


This is illustrated by Figure 7 where without caching, C near words see a 22% increase in perplexity for PTB, and a 32% increase for Wiki, whereas C far see a 28% increase in perplexity for PTB, and a whopping 53% increase for Wiki.

the cache is, in a sense, complementary to the standard model, since it especially helps regenerate words from the long-range context where the latter falls short.


The conjunction of all such constraints defines Q (representing models that exactly agree with human-provided advice).

optimizing the objective reflects a tension between choosing models that increase data likelihood, and emulating language advice.


we can incorporate probability constraints into our adaptation of the PR scheme.


For this task, we found very few of the natural language descriptions to contain quantifiers for some of the individual email categories, making a direct comparison impractical.

in this case, we evaluate methods by combining supervision from descriptions in addition to 10 labeled examples (also in line with evaluation in the original paper).


From the perspective of information flow, BiL-STM passes information from one end of the sentence to the other.

the number of time steps scales with the size of the input.


At each step, each h i captures an increasing larger ngram context, while additionally communicating globally to all other h j via g. The optimal number of recurrent steps is decided by the end-task performance, and does not necessarily scale with the sentence size.

s-LsTM can potentially be both more efficient and more accurate compared with BiLsTMs.


Adding one additional sentence-level node as described in Section 3.2 does not lead to accuracy improvements, although the number of parameters and decoding time increase accordingly.

we use only 1 sentence-level node for the remaining experiments.


In Fig 1 (bottom), the explanation method grad L2 1p places rmax outside the correct (underlined) fragment.

it does not get a hit point, while limsse ms s does.


Global explanation methods are of limited value for explaining a specific prediction as they represent average behavior.


We implement a simplified version of our approach (w/o cell), in which WHERE values come from the question.

this setting differs from Aug.PntNet in the generation of WHERE column.


For instance the ortographically similar words knight and night have large semantic differences.

for a successful semantic application, the model should be able to capture both the regularities, i.e, morphological tags and the irregularities, i.e, lemmas of the word.


However, PSD is considered a simpler task with higher F1 scores 3 .

we believe omitting PSD helps us gain more useful insights on character level models.


Since there is no unique morpheme to meaning mapping, generally multiple morphological tags are used to resolve the morpheme ambiguity.

there is an indirect relation between the number of morphological tags used and the ambiguity of the word.


For each "3" in the intermediate form, it is unclear which "3" in the input to be copied.

we may derive multiple intermediate forms with spurious ones for a training problem.


The fact that meaning representations do not naturally conform to a lin-ear ordering has also prompted efforts to develop recurrent neural network architectures tailored to tree or graph-structured decoding (Dong and Lapata, 2016;Cheng et al., 2017;Yin and Neubig, 2017;Alvarez-Melis and Jaakkola, 2017;Rabinovich et al., 2017;Buys and Blunsom, 2017) Most previous work focuses on building semantic parsers for question answering tasks, such as querying a database to retrieve an answer (Zelle and Mooney, 1996;Cheng et al., 2017), or conversing with a flight booking system (Dahl et al., 1994).

parsers trained on query-based datasets work on restricted domains (e.g., restaurants, meetings; Wang et al.


Furthermore, we quantitatively show that the word-embeddingbased text classification tasks can have the similar level of difficulty regardless of the employed models, using the subspace training (Li et al., 2018) to constrain the trainable parameters.

according to Occam's razor, simple models are preferred.


Although complex compositional functions are avoided in these models, additional modules, such as attention layers, are employed on top of the word embedding layer.

the specific role that the word embedding plays in these models is not emphasized (or explicit), which distracts from understanding how important the word embeddings alone are to the observed superior performance.


Moreover, several recent studies have shown empirically that the advantages of distinct compositional functions are highly dependent on the specific task (Mitchell and Lapata, 2010;Iyyer et al., 2015;Zhang et al., 2015a;Wieting et al., 2015;Arora et al., 2016).

it is of interest to study the practical value of the additional expressiveness, on a wide variety of NLP problems.


For all SWEM variants, there are no additional compositional parameters to be learned.

the models only exploit intrinsic word embedding information for predictions.


For a document, only the word with largest value in each embedding dimension is employed for the final representation.

we suspect that semantically similar words may have large values in some shared dimensions.


(2017), that max-pooling over BiLSTM hidden units outperforms average pooling operation on SNLI dataset.

with only 120K parameters, our SWEM-max achieves a test accuracy of 83.8%, which is very competitive among state-ofthe-art sentence encoding-based models (in terms of both performance and number of parameters) 1 .


We combined the top two into one category, left the next, and combined the bottom three into the lowest category.

for a sentence pair to have a rating of 3, the sentences must have the same meaning, but some unimportant details can differ.


It is hidden by the topic case marker は(wa).

a case analysis model has to find the correct NOM case argument 列車(train).


If the validator is well-trained, it returns scores close to 1 for correct PAS labels that the generator outputs, and 0 for wrong labels.

in Equation 6, the generator tries to predict correct labels in order to increase the scores of fixed validator.


Note that we also use the exophora entities, i.e., an author and a reader, as argument candidates.


Also we use minibatch of 16 sentences for both supervised and unsupervised training of the generator, while we do not use minibatch for validator training.


‡ denotes that the improvement is statistically significant at p < 0.05, compared with Gen+Aug.

we use existing training corpora and word embeddings for the data augmentation.


Moreover, the recent research by Moosavi and Strube (2017) found that the extensive use of lexical and surface features biases entity coreference resolvers towards seen mentions and do not generalize to unseen domains, and the finding can perfectly apply to event coreference resolution.

we propose to improve event coreference resolution by modeling correlations between event corefer-ences and the overall topic structures of a document, which is more likely to yield robust and generalizable event coreference resolvers.


But the local nature of these constraints can lead to fragmented main event chains.

we further model the distributional characteristics of global event chains and encourage the main event chains to have a large number of coreferential mentions and a long stretch (the number of sentences that are present in between the first and last event mention of a chain), to avoid creating partial chains.


5 Each discussion forum document consists of a series of posts in an online discussion thread, which lacks coherent discourse structures as a regular document.

only news articles in the KBP corpora are appropriate for evaluating our approach.


First, at the end of our adversarial training, what we need is a robust generator rather than a discriminator.

second, our generator is to sample data rather than generate new data from scratch; the discriminator is relatively easy to be collapsed.


Data: DS positive set P , DS negative set N G for generator G, DS negative set N D for discriminator D Input: Pre-trained G with parameters θ G on dataset (P , N G ); Pre-trained D with parameters θ D on dataset (P , N D ) Output: Adversarially trained generator G 1: Load parameters θ G for G 2: Split P into the bag sequence Load parameters θ D for D 5: Compute the probability Obtain the generated part T by sampling according to {p G (s j )} j=1...|B| and the rest set Calculate the reward r 12: 13: end for Compute the accuracy Optimizing Generator The objective of the generator is similar to the objective of the one-step reinforcement learning problem: Maximizing the expectation of a given function of samples from a parametrized probability distribution.

we use a policy gradient strategy to update the generator.


In order to better reflect the quality of the generator, we define the reward r from two angles: • As the common setting in adversarial learning, for the generated sample set, we hope the confidence of being positive samples by the discriminator becomes higher.

the first component of our reward is formulated as below: the function of b 1 is to reduce variance during reinforcement learning.


To this end, we first briefly introduce the dataset and the evaluation metrics.

empirically, the adversarial learning process, to some extent, has instability; we next illustrate the convergence of our adversarial training process.


discriminator performs well on N D ; moreover, N D is not used during adversarial training.

the accuracy on N D is the criterion to reflect the performance of the discriminator.


It is because that the chance we give the generator to challenge the discriminator is just one time scanning of the noisy dataset; this critical point is yielded when the generator has already been robust enough.

we stop the training process when the model reaches this critical point.


Even the joint models based on neural network (Zheng et al., 2017), it only assigns a single tag to a word, which means one word can only participate in at most one triplet.

the triplet overlap issue is not actually addressed.


As a result, one word can participate at most one triplet.

the NovelTagging model can only recall a small number of triplets, which harms the recall performance.


Relying on the feature vector o g , it attempts to accurately predict the probability of the token x triggering an event for all event classes, i.e.,ŷ = D(o g , θ d ), and assigns x to the most probable class c (iffŷ c > ∀ŷc,c = c).

g and D cooperate with each other during training, developing the parameters θ g and θ d with the same goal -to minimize the performance loss L(ŷ, y) in the detection task: where, y denotes the ground-truth probability distribution over event classes, and L indicates the deviation of the prediction from the ground truth.


The latent features it uses are well-produced by G, and decontaminated by eliminating possible fakes like those made byǦ.

in experiments, we choose to output the prediction results of D. In this paper, we use two recurrent neural networks (RNN) (Sutskever et al., 2014;Chung et al., 2014) of the same structure as the generators.


Similarly, they are defined as a d-dimensional vector in R d .

lSTM works in the following way: where W ∈ R 4d×(d+e) and b ∈ R 4d are parameters of affine transformation; σ refers to the logistic sigmoid function and denotes element-wise multiplication.


This results in twofold accumulation of feature information, causing a serious overfitting problem.

hybrid is localized to much higher precision but substantially lower recall.


In this case we want the weight vector to shift each time, so M can diversify fast.

we use a shift function similar to the canonical NTM.


Recall that our model has a uniform architecture for all input types and is trained on eventevent, event-timex and event-DCT pairs simultaneously.

its performance is not optimal for some lower-frequency pair types.


The double temporality states that a narrative story often describes a sequence of events following the chronological order and therefore, the temporal order of events matches with their textual order (Walsh, 2001;Riedl and Young, 2010;Grabes, 2013).

we can easily distill temporal event knowledge if we have identified a large collection of narrative texts.


However, narrative texts are common in other genres as well, including news articles and novel books, where little annotated data is readily available.

in order to identify narrative texts from rich sources, we develop a weakly supervised method that can quickly adapt and identify narrative texts from different genres, by heavily exploring the principles that are used to characterize narrative structures in narratology studies.


A narrative usually describes events caused or experienced by actors.

a narrative story often has one or two main characters, called protagonists, who are involved in multiple events and tie events together.


Furthermore, the subject of the sentence is meant to represent a character.

we specify 12 grammar rules 4 to require the sentence subject noun phrase to have a simple structure and have a proper noun or pronoun as its head word.


An  As we know, coverage on acquired knowledge is often hard to evaluate because we do not have a complete knowledge base to compare to.

we propose a pseudo recall metric to evaluate the coverage of event knowledge we acquired.


Such systems rely on end-to-end training on large amounts of data, making no prior assumptions about linguistic structure and focusing on stastically frequent patterns.

they somehow step away from computational linguistics as they learn implicit linguistic information automatically without aiming at explaining or even exhibiting classic linguistic structures underlying the decision.


Such works may help us to highlight the linguistic features learned by a CNN.

our method takes inspiration from those works.


2015; Noh et al demonstrate the efficiency of deconvolution networks to predict segmentation masks to identify pixel-wise class labels.

deconvolution is able to localize meaningful structure in the input space.


However, we cannot assume both dimensions the sequence of words and their embedding representation are isotropic.

the filters of CNNs for text typically differ from their counterparts designed for images.


Thus the filters of CNNs for text typically differ from their counterparts designed for images.

in text, the width of the filter is usually equal to the dimension of the embedding, as illustrated with the red, yellow, blue and green filters in figure 1 Using CNNs has another advantage in our context: due to the convolution operators involved, they can be easily parallelized and may also be easily used by the CPU, which is a practical solution for avoiding the use of GPUs at test time.


For castra, the first co-occurent for Livy is hostis (z-test 22.72), before castra (z-test 10.18), ad (z-test 10.85), in (z-test 8.21), impetus (z-test 7.35), que (z-test 5.86) while in Caesar, impetus does not appear and the scores of all other lemmas are lower except castra (z-test 15.15), hostis (8), ad (10,35), in (5,17), que (4.79).

our results suggest that CNNs manage to account for specificity, phrase structure, and cooccurence networks.


Since the performance of PA is lower than that of English CR, there might be a more significant gap between training and testing.

scheduled sampling (Bengio et al., 2015) is adopted to bridge the gap: in training, the oracle entity assignment is used with probability ϵ t (at the t-th iteration) and the system output otherwise.


The performance of Japanese CR is lower than that of English CR.

we assume there are improved/worsen examples, and our CR performance did not improve significantly.


This suggests that the interaction between the occurrence of linguistic markers and the power status of speakers exists within a subset of the linguistic categories, but not across all of them.

we consider this first experiment a replication of past findings of the effect of social power on alignment: social power has a significant effect across certain markers, but its overall effect is neutralized in the full model since some markers at not significant.


Additionally, the results also suggest that the influence of power on linguistic alignment is better characterized by the more fine-grained cumulative effect of linguistic markers than when it is simply explained by the mere difference between their absence or presence.

we will discard C presence and proceed with C count .


In particular, our findings suggest that the probability change of LIWC categories is more likely to be a case of automatic alignment, rather than an intentional accommodation, because it is better explained by lowerlevel linguistic features (utterance length).

we suggest that future work on social power and language use should consider other (maybe higher-level) linguistic elements.


With these developments, an increasing number of tutorials and online references are being published daily.

the task of students, educators and researchers of tracking the changing landscape in this field has become increasingly difficult.


The backbone of our Taxonomy corresponds to the syllabus of a university-level NLP course and was expanded to include related topics from other courses in ML, IR and AI.

there is a bias in the corpus towards NLP resources and resources from other fields in so far as they are relevant to NLP.


Concretely, we show that LCB incentivizes GEC systems to avoid correcting even when they can generate a valid correction.

existing systems obtain comparable or superior performance compared to humans, by making few but targeted changes to the input.


To study our estimators offline, we preemptively collected 10,000 human judgments which cover several  1: (a) At a system-level, automatic metrics (ROUGE-L) and human judgment correlate well, but (b) the instance-level correlation plot (where each point is a system prediction) shows that the instancelevel correlation is quite low (ρ = 0.31).

if we try to locally improve systems to produce better answers ( in (a)), they do not significantly improve ROUGE scores and vice versa ( ).


This bias also appears in pool-based evaluation for knowledge base population (Chaganty et al., 2017).

the problems with automatic metrics clearly motivate the need for human evaluation, but can we still use the automatic metrics somehow to save costs?


Prior work has shown that existing automatic metrics have poor instance-level correlation with mean human judgment and that they score many good quality responses poorly.

the evaluation is systematically biased against genuine system improvements that would lead to higher human evaluation scores but not improve automatic metrics.


The relative importance is measured as the gain over previously selected sentences.

each time the proposed model selects one sentence, it scores the sentences considering both sentence saliency and previously selected sentences.


McDonald (2007) treats sentence selection as an optimization problem under some constraints such as summary length.

he uses Integer Linear Programming (ILP) to solve this optimization problem.


Considering that the F1 gain value might be negative in the labeled data, we follow previous works (Ren et al., 2017) to use Min-Max Normalization to rescale the gain value to [0, 1]: We then apply a softmax operation with temperature τ (Hinton et al., 2015) 1 to produce the labeled data distribution Q as the training target.

we apply the temperature τ as a smoothing factor to produce a smoothed label distribution Q: we minimize the KL loss function J: 5 Experiments A large scale dataset is essential for training neural network-based summarization models.


The highlights are created by human editors and are abstractive summaries.

the highlights are not ready for training extractive systems due to the lack of supervisions.


We found that larger size GloVe does not lead to improvement.

we use 50-dim word embeddings for fast training.


The result of SUMMARUNNER is on the anonymized dataset and not strictly comparable to our results on the non-anonymized version dataset.

we also include the result of LEAD3 on the anonymized dataset as a reference.


In the range of sentence 7 to 13, NN-SE barely extracts any sentences, but our NEUSUM model still extract sentences in this range.

we think this is one of the reasons why NEUSUM performs better than NN-SE.


However, it might exceed the maximum size allowed, and still contain some redundancy or off-topic sections unrelated to the general theme of the meeting (e.g., chit-chat).

we design the following submodular and monotone non-decreasing objective function: where λ ≥ 0 is the trade-off parameter, n s i is the number of occurrences of word s i in S, and w s i is the CoreRank score of s i .


In addition, the MSCG is by design robust to noise, and our custom path re-ranking strategy, based on graph degeneracy, makes it even more robust to noise.

our framework is advantaged on ASR input.


Our abstractor is a simple encoder-aligner-decoder model (with copying) and is trained on pseudo document-summary sentence pairs obtained via simple automatic matching criteria.

our method incorporates the abstractive paradigm's advantages of concisely rewriting sentences and generating novel words from the full vocabulary, yet it adopts intermediate extractive behavior to improve the overall model's quality, speed, and stability.


for low-level vs. high-level parameters.

related tasks should share some common representations (e.g., high-level information), as well as need their own individual task-specific representations (esp.


We believe that while textual summarization, question generation, and entailment generation have different training data distributions and low-level representations, they can still benefit from sharing their models' high-level components (e.g., those that capture the skills of saliency and inference).

we keep the lower-level layer (i.e., first layer closer to input words) of the 2layer encoder of all three tasks unshared, while we share the higher layer (second layer in our model as shown in Fig.


Hard-sharing: In the most common multi-task learning hard-sharing approach, the parameters to be shared are forced to be the same.

gradient information from multiple tasks will directly pass through shared parameters, hence forcing a common space representation for all the related tasks.


Other platforms, review domains and languages While we focused on Amazon product reviews written in English, the majority of the features discussed in Section 3 are platform-, domainand language-independent, and the existence and importance of moderating factors described in Section 4 is by no means limited to product reviews.

we encourage researchers to evaluate the usefulness of these features and study these moderating factors in different domains, platforms, and languages, possibly identifying new features and moderating factors.


We cannot directly calculate the similarity between the monolingual word vectors of W and U , because they are trained separately and the semantics of dimension are not aligned.

the challenge is to devise a way to compute similarities across two different vector spaces while retaining their respective cultural characteristics.


Considering situations where many slang terms have literal meanings, it may be unfair to retrieve target terms from such machine translators by solely inputing slang terms without specific contexts.

we utilize example sentences of their slang meanings from some websites (mainly from Urban Dictionary 11 ).


The MFD, similar to LIWC (Pennebaker et al., 2001;Tausczik and Pennebaker, 2010), associates a list of related words with each one of the moral foundations.

analyzing text equates to counting the number of occurrences of words in the text which also match the words in the MFD.


Our approach is guided by the intuition that the abstract moral foundations will manifest differently in text, depending on the specific characteristics of the events discussed in the tweet.

it is necessary to correctly model the relevant contextualizing information.


Also note that the distribution of l(x, z * ) has smaller variance and is more concentrated.

the inference model receives informative gradient updates to discriminate between gold and imperfect  samples.


Compared with logical forms, semantic graphs have a tight-coupling with knowledge bases (Yih et al., 2015), and share many commonalities with syntactic structures (Reddy et al., 2014).

both the structure and semantic constraints from knowledge bases can be easily exploited during parsing (Yih et al., 2015).


As described above, each action has two parts, one for structure (e.g., add edge), and the other for semantic (e.g., next to).

actions may share the same structure or semantic part, e.g., add edge:next to and add edge:loc have the same structure part, and add node:A and arg node:A have the same semantic part.


In recent years, one promising direction of semantic parsing is to use semantic graph as representation.

semantic parsing is modeled as a semantic graph generation process.


Note that ∆ = 0 means the graphs are isospectral, and the metric goes to infinite.

the higher ∆ is, the less similar the graphs (i.e., their Laplacian spectra).


The first two steps have been shown to be beneficial in previous work (Artetxe et al., 2016), while the second length normalization guarantees the final embeddings to have a unit length.

the dot product of any two embeddings is equivalent to their cosine similarity and directly related to their Euclidean distance 1 , and can be taken as a measure of their similarity.


Due to space limitations, we only show curves for Dutch Name Tagging, while we observe similar results on other tasks.

we may conclude that our model does not heavily rely on the amount of auxiliary task data.


As additional retrieval limitation, we force the number of facts per answer candidate to be the same, in order to avoid a frequency bias for an answer candidate that appears more often in the knowledge source.

if we select the maximum 100 facts for each task instance and we have 10 answer candidates a i=1..10 , we retrieve the top 10 facts for each candidate a i that has either a subject or an object lemma for a token in a i .


Each retrieved fact is represented as a triple f = (w subj , where w subj 1..L subj and w obj 1..L obj are a multi-word expressions representing the subject and object with sequence lengths L subj and L obj , and w rel 0 is a word token corresponding to a relation.

3 of fact encoding, we obtain a separate knowledge memory for each instance in the data.


We use an attention function Att, scale the scalar attention value using sof tmax, multiply it with the value representation M v i and sum the result into a single vector value representation c kn s i : (10) Att is a dot product, but it can be replaced with another attention function.

of this operation, the context token representation c ctx s i and the corresponding retrieved knowledge c kn s i are in the same vector space ∈ R 2h .


In our work, we are retrieving (typically) distinct sets of knowledge facts that might (or might not) be relevant for understanding the story and answering the question.

in our setup, we crucially depend on the ability of the attention mechanism to retrieve relevant pieces of knowledge.


Thus we must relax the condition of literal meaning to intent or affect of the utterance within the context of the discourse.

if the intent is to criticize a restaurant's service in a review, changing "salad" to "chicken" could still have the same effect but if the intent is to order food that substitution would not be acceptable.


The presence of a question is imperative in DMN+, as it helps to determine the initial state of the episodic memory module.

we prepend an interrogative phrase such as "Who is" or "What is" to every entity name.


Furthermore, due to the limitations of automatic metrics on evaluating story quality, reinforcement learning methods with hand-crafted rewards also face difficulties in gaining an overall performance boost.

we propose an Adversarial REward Learning (AREL) framework to learn an implicit reward function from human demonstrations, and then optimize policy search with the learned reward function.


Particularly, our method gains slight performance boost over the baseline systems on automatic metrics; human evaluation, however, indicates significant performance boost.

we further discuss the limitations of the metrics and validate the superiority of our AREL method in performing more intelligent understanding of the visual scenes and generating more human-like stories.


Inverse Reinforcement Learning Reinforcement learning is known to be hindered by the need for an extensive feature and reward engineering, especially under the unknown dynamics.

inverse reinforcement learning (IRL) has been proposed to infer expert's reward function.


On the other hand, the objective J θ of the reward function is to distinguish between humanannotated stories and machine-generated stories.

hence it is trying to minimize the KL-divergence with the empirical distribution p e and maximize the KL-divergence with the approximated policy distribution π β : Since h(π β ) and h(p e ) are irrelevant to θ, we denote them as constant C. the objective J θ can be further derived as here we propose to use stochastic gradient descent to optimize these two models alternately.


The inconsistency of automatic evaluation and human evaluation lead to a suspect that these hand-crafted metrics lack the ability to fully evaluate stories' quality due to the complicated characteristics of the stories.

we conduct experiments to analyze and discuss the defects of the automatic metrics in section 4.2.


Automatic metrics cannot fully evaluate the capability of our AREL method.

we perform two different kinds of human evaluation studies on Amazon Mechanical Turk: Turing test and pairwise human evaluation.


Unlike automatic metric evaluation, the Turing test has indicated a much larger margin between AREL and other competing algorithms.

we empirically confirm that metrics are not perfect in evaluating many implicit semantic properties of natural language.


Then connecting the sentences together, we observe that the AREL story is more coherent and describes the photo stream more accurately.

our AREL model significantly surpasses the XEss model on all the three aspects of the qualitative example.


They use orthogonal iterations to estimate a moving average of the covariance matrices, which improves memory consumption.

we base our novel optimization algorithm for DPCCA on this approach.


Picturebook embeddings on their own are likely to be useful for representing concrete words but it is not clear whether they will be of benefit for abstract words.

we would like to fuse our Picturebook embeddings with other sources of information, for example Glove embeddings (Pennington et al., 2014) or randomly initialized embeddings that will be trained.


It is often presupposed in our communication and not explicitly stated.

it is difficult to extract naive action-effect relations from the existing textual data (e.g., web).


Then, each student was also asked to provide positive or negative labels for the images collected by the other two students.

each image has three positive/negative labels.


We can see that the images returned from the query "book is set aflame" are not depicting the physical effect state of "burn a book".

it's important to identify images with relevant effect states to train the model.


For simplicity and space issue, we denote the operation of an LSTM unit on x i as LSTM(x i ).

the contextualized word representation h (0) i ∈ R 2dim h is obtained as follows: The above word-level representation has not considered the target information yet.


Then we perform convex combination of h i based on the gate: Here, denotes element-wise multiplication.

the non-recursive form of this equation is as follows (for clarity, we ignore the subscripts): (1 − t (k) )] h (0) the context information is integrated in each upper layer and the proportions of the contextualized representations and the transformed representations are controlled by the computed gates in different transformation layers.


In these two sentences, sentiment contexts are both "high" (i.e., same attention), but sentence (3) is negative and sentence (4) is positive simply because their target aspects are different.

focusing on improving attention will not help in these cases.


However, for an unlabeled corpus no such statistical technique is applicable.

identification of words which are significant with a consistent polarity across domains is a non-trivial task.


In such a case where a word results in a pvalue smaller than the critical p-value (0.05), we reject the null-hypothesis.

we assume that the word w belongs to a particular class (positive or negative) in the data, hence it is a significant word for classification (Sharma and Bhattacharyya, 2013).


Essentially, the frequent occurrence of the word in the corpus allows it to be in context of other words frequently.

a polar word having the highest frequency in the target domain is observed to be more accurate as pivot for identification of polarity of input words.


10 The classifier C s trained on the SCP features (transferred knowledge) from the source domain and the classifier C t trained on self-discovered target specific features from the pseudo labeled target domain instances bring in complementary information from the two domains.

combining C s and C t in a weighted ensemble (WSM) further enhances the cross-domain performance.


Since electronics and movie are two very dissimilar domains in terms of domain specific words, unlike, books and movie, getting a high accuracy classifier in the movie domain from the electronics domain is a challenging task (Pang et al., 2002).

in Table 4 results are reported with electronics as the source domain and movie as the target domain.


The emotionalization module is responsible for adding sentiment to the neutralized semantic content for sentiment-to-sentiment translation.

in cycled training, given an emotional sentence with sentiment s, we first neutralize it to the nonemotional semantic content, and then force the emotionalization module to reconstruct the original sentence by adding the sentiment s. the emotionalization module is taught to add sentiment to the semantic context in a supervised way.


The proposed method requires the two modules to have initial learning ability.

we propose a novel pre-training method, which uses a self-attention based sentiment classifier (SASC).


Experimental results show that this method achieves much lower results than explicitly removing emotional words based on discrete attention weights.

we do not choose this method in our work.


Because of the discrete choice of neutral words, the loss is no longer differentiable over the neutralization module.

we formulate it as a reinforcement learning problem and use policy gradient to train the neutralization module.


We can achieve this performance by also reducing the computation times of the RN considerably.

we hope that this contribution may allow applying RNs to larger problems.


However, embeddings are typically used as pre-trained building blocks for subsequent tasks.

their learning is usually the pre-computation step for the more complex downstream models and spending more time is justified, especially with GPU acceleration.


Furthermore, detection tasks are commonly evaluated using F-measure on positive classes, rather than accuracy or F-measure on all classes.

positive and negative classes play different roles in the evaluation: the performance is evaluated by only considering how well we can detect positive instances, while correct predictions of negative instances are ignored.


The importance varies from different classes (i.e., T P and T N are asymmetric).

to make the training procedure consistent with F-measure, it is critical to take this importance difference into consideration.


Specifically, given the current model of neural networks parameterized by θ, let w β (θ) denote the relative importance of negative instances to positive instances for F β -measure.

then w β (θ) can be computed as the ratio of marginal negative utility M U F β (t N (θ)) to the marginal positive utility M U F β (t P (θ)), where t P (θ) and t N (θ) are t P and t N on training data with respect to θ-parameterized model: then at each iteration of the model optimization (i.e., each step of gradient descending), we want the model to take next update step proportional to the gradient of the w β -scaled cross-entropy loss function L AS (θ) at the current point: based on the contributions that correctly predicting one more instances of each class bringing to F-measure, the training procedure dynamically adjusts its attention between positive and negative instances.


Besides, if the accuracy of positive class is close to zero, F-measure will also be close to zero no matter how high the accuracy on negative class is, i.e., correctly predicting negative instances can result in little F-measure increment.

negative instances are inconsequential when the accuracy on positive class is low.


Limited by the size of training data, the models with millions of parameters may not be well generalized.

it is She see Tom is catched by policeman in park at last night.


However, such errorcorrected sentence pairs are not sufficiently available.

many neural GEC models are not very well generalized.


It is clear that our approach can generate less fluent sentences with various grammatical errors and most of them are typical mistakes that a human learner tends to make.

they can be used to establish high-quality training data with their correct sentence, which will be helpful for increasing the size of training data to numbers of times, accounting for the improvement by fluency boost learning.


On the other hand, computing distance between n(n−1)/2 pairs of instances to construct the graph is also very expensive (Muja and Lowe, 2014).

we choose to use k-nearest neighborbased approach as it has been successfully used in other study (Steinbach et al., 2000).


Also, modeling the high-level decision-making policy in dialogs enables useful generalization and dataefficient domain adaptation (Gašić et al., 2010).

the motivation of this paper is to develop an unsupervised neural recognition model that can discover interpretable meaning representations of utterances (denoted as latent actions) as a set of discrete latent variables from a large unlabelled corpus as shown in Figure 1.


Since the context c can be any dialog history, if the meaning of each class changes given a different context, then it is difficult to extract an intuitive interpretation by only looking at all responses with class k ∈ [1, K].

the second property looks for latent actions that have context-independent semantics so that each assignment of z conveys the same meaning in all dialog contexts.


To map the latent samples to the initial state of the decoder RNN, we define {e 1 ...e m ...e M } where e m ∈ R K×D and D is the generator cell size.

the initial state of the generator is: Finally, the generator RNN is used to reconstruct the response given h G 0 .


A natural solution to correct the anti-information issue in Eq.

3 is to maximize both the data likeli-hood lowerbound and the mutual information between z and the input data: jointly optimizing ELBO and mutual information simply cancels out the informationdiscouraging term.


For example, the dialog act is a wellknown utterance feature and depends on dialog state (Austin, 1975;Stolcke et al., 2000).

we introduce a second type of latent action based on sentence-level distributional semantics.


Also simply augmenting the inputs of the decoders with latent action does not guarantee that the generated response exhibits the attributes of the give action.

we use the controllable text generation framework (Hu et al., 2017) by introducing L Attr , which reuses the same recognition network q R (z|x) as a fixed discriminator to penalize the decoder if its generated responses do not reflect the attributes in z.


We found that standard training failed to learn informative latent actions for either DVAE or DVST because of the posterior collapse.

kL-annealing (Bowman et al., 2015) and bag-of-word loss  are used to force these two models learn meaningful representations.


using mini-batch is an efficient trade-off between q(z) estimation and computation speed.


Normalized Inverse Response Frequency (NIRF) is based on the assumption that a response is more general if it corresponds to more input utterances in the corpus.

we use the inverse frequency of a response in a conversation corpus to indicate its specificity level.


Event clustering phenomena cause a sequence to resemble itself in a self-similar manner.

studies of the fluctuation underlying a sequence can take another form of long-range correlation analysis, to consider the similarity between two subsequences underlying a time series.


Most individual clusters exhibit a period of time in which only a few early adopter (Rogers Everett, 1995) members have the merger, a period of rapid diffusion of the merger, then some time where a few laggards resist the merger.


Once the cycle in i has generated a b, it is impossible for ii to generate an a on one side of the b and a c on the other.

i must always be derived strictly later than ii, as shown in iv.


In the other direction, if the last tree of an n + 1 tree derivation in G A is of the shape in Figure 3(d), then this implies the 4 It is easy to verify by inspection of Figure 3 that whenever one rule from G can be applied to the output of another rule, then the tree pairs in GXA which correspond to these rules can compose with one another.

we can add the new tree to the existing derivation and be assured that it will compose with one of the trees that is already present.


Only nonterminals in NXA will be involved in this derivation, and by construction there is at most one such nonterminal per tree.

a well-formed derivation structure in GXA will never branch, and we can refer to the n + 1th tree pair as the one which is at depth n in the derivation structure.


Observe that, by construction, for all X, A ∈ N every target tree rooted in S XA is prefix lexicalized.

the trees created in step 4 are all prefix lexicalized variants of non-lexicalized tree pairs; steps 5-6 then remove the non-lexicalized trees from the grammar.


Observe that our transformation does not cause nonterminals which were linked in the original grammar to become unlinked, as noted for example in Figure 4.

subtrees which are gener-  76 14 66% 1.47 ITG (10000 translation pairs) 10,003 170,000 99.97% 1.31 Table 1: Grammar sizes before and after prefix lexicalization, showing O(n 2 ) size increase instead of the worst case O(n 3 ).


The order induced by the syntactic distances fully specifies the order in which the sentence needs to be recursively split into smaller constituents (Figure 1): in case of a binary tree, there exists a oneto-one correspondence between the ordering and the tree.

our model is trained to reproduce the ordering between split points induced by the ground-truth distances by means of a margin rank loss (Weston et al., 2011).


Given that only the ranking induced by the ground-truth distances in d is important, as opposed to the absolute values themselves, using an MSE loss over-penalizes the model by ignoring ranking equivalence between different predictions.

we propose to minimize a pair-wise learning-to-rank loss, similar to those proposed in (Burges et al., 2005).


In addition, with a continuous space of subtypes in a LVeG, similarity between subtypes can be naturally modeled by their distance in the space and can be automatically learned from data.

with continuous and smooth weight functions, fine-grained production rules over similar subtypes would have similar weights in LVeGs, eliminating the need for the extra smoothing steps that are necessary in training LVGs.


In addition, different parse trees of the same substring typically lead to different point estimations of the subtype vector at the root nonterminal.

cVGs cannot use dynamic programming for inference and hence have to resort to greedy search or beam search.


A higher dimension or a larger Gaussian component number hurts the model performance and requires much more time for learning.

model WSJ T  S  T  S  T  S  T  S  T  S  T  S  T  S  T  S  T  S   LVG-D-   our choice of K r = 4 and d = 3 in Gm-LVeGs for parsing is a good balance between the efficiency and the parsing accuracy.


This does not affect the ELMo component of the word embeddings.

even common words are replaced with probability at least 1 10 , making the model rely on the ELMo embeddings instead of the learned embeddings.


4 The split we used is not standard for part-of-speech tagging.

we do not compare to part-of-speech taggers.


may each involve certain stylistic, syntactic, and lexical choices, resulting in meaningfully different distributions of word cooccurrences.

such factors may be encoded in the word embeddings, and input corpora may be differentially informative towards various NLP tasks.


In addition, metaphor identification is highly dependent on its context.

phrase-level models (e.g., Tsvetkov et al.


W i and W o are updated via back propagation of errors.

only the value of the position that represents the centre word's probability, i.e., p(t|c 1 , ..., c n , ..., c m ), will get close to the value of 1.


This is likely due to the fact that Skip-gram is trained by using a centre word to maximise the probability of each context word, whereas CBOW uses the average of context word input vectors to maximise the probability of the centre word.

skip-gram performs better in modelling one-word context, while CBOW has better performance in modelling multi-context words.


We believe that one of the key purposes of metaphor processing is to support other NLP tasks.

we conducted another set of experiments to evaluate how metaphor processing can be used to support English-Chinese machine translation.


3 shows, for the item incredible → [in, not], [believe], [able, capable] in the word map, the latent meanings have different biases towards "incredible".

we assign different weights to latent meanings.


Notice that up to this point, the prior has hardly been updated.

moving the variational approximation back to the prior would likely reduce the reconstruction term since the standard normal prior is not useful for inference purposes.


The scale started from 0 and was increased by 1 /20,000 after each mini-batch.

at iteration t the scale is min( t /20,000, 1).


Compared with the former, the latter method has a relatively simple model structure, so that a larger corpus can be used for training and the model can be trained within reasonable time, hence is preferred from the viewpoint of computation.

we focus on this kind of methods in this paper.


The proposed architecture reportedly improves over the previous best results on the WMT 2014 English-to-German and English-to-French translation tasks, and we verified its strong performance on our data set in preliminary experiments.

we consider it a strong state-of-the-art baseline for our experiments.


This is a first indicator that discourse phenomena are the main reason for the observed improvement, rather than topic effects.

we focus solely on using the previous sentence in all subsequent experiments.


This is due to the enormity of factors g θ (y t ; y −t ) over a large number of translation variables y t 's (i.e., the number of sentences in the document) as well as their unbounded domain (i.e., all sentences in the target language).

we resort to maximising the pseudo-likelihood (Besag, 1975) for training the parameters: where D is the set of bilingual training documents, and |d| denotes the number of (bilingual) sentences in the document .


However we also need to identify locations and want to include both proper names (e.g., Disneyland) as well as nominals (e.g., store, beach), so Named Entity Recognition will not suffice.

we extract (Loc, Act) pairs using syntactic patterns.


We randomly sampled 1000 documents from a Microsoft question answering forum and found out that only 7% of the acronyms co-occur with the corresponding meanings in the same document, which means 93% of the time when the user does not understand an acronym, she will need to find clues outside of the document.

it is particularly useful to develop a system that can automatically resolve the true meanings of acronyms in enterprise documents.


However, in enterprises, we do not have a knowledge base with anchor links.

we cannot calculate popularity in the same way.


So we only included the TFIDF cosine similarity as the context similarity feature in our system.

the learned ranking model may lack the capability to properly rank the external meanings.


We have discussed that one particular motivation for confidence estimation is that the candidate ranking stage has some bias so it does not always rank public meanings at top when they are correct.

assuming the confidence estimation model can remove incorrect top-ranked result, we still need one additional step to decide if any public meaning is correct, which we call a final selection model.


In contrast, our AD system mines disambiguation features directly from the enterprise corpus and utilizes them together with the public signals.

it can more accurately represent the characteristics of the enterprise and lead to much better disambiguation performances.


These model parameters alone may not be sufficient for generating natural conversations.

despite providing enormous amount of data, neural generative systems have been found to be ineffective for use in real world applications (Liu et al., 2016).


The conversation context consists of an ordered sequence of utterances and each utterance can be further viewed as a sequence of words.

context can be viewed as having multiple levels of hierarchies-at the word level and then at the utterance (sentence) level.


all the verbs in a response are mapped to a set of manually identified list of 192 verbs.


Our goal is to analyze how the start of a civil conversation is tied to its potential future derailment into personal attacks.

we only consider conversations that start out as ostensibly civil, i.e., where at least the first exchange does not exhibit any toxic behavior, 4 and that continue beyond this first exchange.


Attention allows the NMT system to dynamically choose which parts of the source are most important when deciding on the current output token.

there is no longer a static vector representation of the sentence available in the system.


In machine translation, for example, a sentence in one language can be translated in multiple ways to another language.

bLEU takes an n-gram based approach on the surface forms, while METEOR considers only unigram matches but uses stemming and controls for synonyms.


In this section, we propose two new variants of RNNGs that construct trees using a different con- 12 As the beam search procedure is time-consuming, we randomly sample 500 cases for each attractor and compute the number agreement accuracy on these samples.

13 the correct and incorrect forms of the sentence might have different partial trees, as the highest scoring beam entries may be different for each alternative.


The activated slot Japanese will be replaced as French, which our system can learn.

an important pattern is the machine-response (e.g., "there is no [XXX constraint]") in the immediate previous utterance.


<food>) as the first decoder input, which is also mapped to a trainable embedding E type .

the starting index s 0 of the slot value is computed as the following, where u 0 i is the attention score of the i th word in the input against the decoder state d 0 .


The model is totally unaware of the different ways of referring to the same entity.

the derived dialogue states may not have canonical forms that are consistent with the values in the backend database, making it more difficult to retrieve the correct results.


Taking a closer look at the data, as shown in the histogram in Figure 2, the majority of the food type slot appears more than 10 times in the training data.

the model oftentimes only learns to memorize these frequent slot values, and not the contextual patterns which can be more crucial for extracting slot values not known in advance.


In addition, our model is quite general without complicated taskspecific designs.

we show that Mem2Seq can be trained faster and attain the state-of-the-art performance on three different task-oriented dialog datasets.


For example, we represent the information of The Westin in Table 1: (The Westin, Distance, 5 miles).

we sum word embeddings of the subject, relation, and object to obtain each KB memory representation.


We believe humans can produce good answers even with a low BLEU score, since there could be different ways to express the same concepts.

mem2Seq shows the potential to successfully choose the correct entities.


In fact, unlike chat-bots and open domain dialogs where BLEU score does not correlate with human evaluation , in task-oriented dialogs the answers are constrained to particular entities and recurrent patterns.

we believe BLEU score still can be considered as a relevant measure.


The current Seq2Seq model fails to meet these diverse requirements, by using a general average likelihood as the optimization criteria.

it usually generates safe and commonplace responses, such as 'I don't know'.


Although the current Seq2Seq model is capable to generate fluent responses, these responses are usually general.

many researchers focused on how to improve the generation quality and specification.


If we optimize an averaged likelihood, we can only capture the common matching patterns, which leads to generating common responses.

if we want to generate specific responses, we need to capture the most significant matching pattern; while if we want to generate diverse responses, we need to define a criteria which has the ability to capture the various matching patterns.


To meet the specific requirement, we need to capture a specific matching pattern between post and response, rather than the common matching pattern.

instead of optimizing the averaged likelihood, we turn to use the maximum generated likelihood (MGL) as the objective function.


To meet the diverse requirements, we need to capture various matching patterns between post and its multiple ground-truth responses.

instead of optimizing the averaged likelihood, we turn to optimize the conditional valueat-risk, named CVaR for short.


Applying CVaR to generating diverse responses, we can define the random cost Z as − log P (Y |X), the corresponding CVaR is: where ν r (θ) = inf{ν ∈ R|P (− log P (Y |X) ≤ ν) ≥ r}, and θ are parameters of the Seq2Seq model.

we have: for a given post X and its ground- ), optimizing CVaR is equivalent to maximizing the following objective function: where Y 1−α is a collection of ground-truth responses such that: We can see that maximizing the above objective function requires the worst 1 − α responses to be close to the post.


Therefore, we have: Therefore, for a given post X and its ground- ), optimizing CVaR is equivalent to maximizing the following objective function: where Y 1−α is a collection of ground-truth responses such that: We can see that maximizing the above objective function requires the worst 1 − α responses to be close to the post.

we aim to capture each distinct post-response matching pattern by optimizing the CVaR criteria, which can meet the requirement for generating diverse responses.


For maximum generated likelihood(MGL) model, some people may argue that the specific results may be due to the usage of single postresponse pair.

we also implement the baseline of using a single post-response pair, by random selecting the response from the ground-truth for each post, denoted as Single Model.


That's because it has the capability to capture various matching patterns in the training data, by optimizing the worst 1 − α costs.

our CVaR model produces both fluent and diverse results, as compared with baseline methods.


During response generation, if an entity is overused, the response diversity will be reduced.

once a knowledge item occurred in the response, the corresponding coefficient should be reduced in case that an item occurs multiple times.


In the training process, our model needs to sample the latent variable from the posterior distribution P (z|Y, c), which is intractable.

the recognition network q φ (z|Y, c) is introduced to approximate the true posterior distribution so that we can sample z from this deterministic parameterized model.


Since our model needs the sentence function label for each pair, we built a classifier to predict the sentence function automatically to construct large-scale labeled data.

we sampled about 2,000 pairs from the original dataset and annotated the data manually with four categories, i.e., interrogative, imperative, declarative and other.


However, current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information.

we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective.


All the dialog modules were trained together instead of separately.

our method is end-to-end trainable and doesn't require human expert involvement.


And user sentiment is a direct reflection of user satisfaction.

we detected the user sentiment scores from multimodal sources on the fly, and used them as immediate rewards in an adaptive end-to-end dialog training setting.


When the system repeatedly asks for the same entity, they express stronger sentiment.

summary statistics that record how many times certain entities have been asked during the conversation is representative of users' accumulating sentiment.


With 1664 editions, it is computationally expensive to consider all editions si-multaneously (more than 10 6 dictionaries).

we split the set of editions in 10 pivot and 1654 remaining editions, and do not compute nor use dictionaries within the 1654 editions.


We refer to all editions (including pivot editions) as target editions.

a pivot edition has two roles: as a pivot language and as a target edition.


We also showed that randomly generated CM data doesn't improve the LM.

the linguistic theory based generation is of crucial significance.


As shown in Figure 1, we construct a wordcharacter lattice by matching a sentence with a large automatically-obtained lexicon.

word sequences such as "长江大桥 (Yangtze River Bridge)", "长江 (Yangtze River)" and "大 桥 (Bridge)" can be used to disambiguate potential relevant named entities in a context, such as the person name "江大桥 (Daqiao Jiang)".


On the other hand, no segmentation is available for the MSRA test sections, nor the Weibo / resume datasets.

ontoNotes is leveraged for studying oracle situations where gold segmentation is given.


The sparsity ratio of this 4-order tensor for all the datasets is of the order 1e-7.

of the extreme sparsity, this approach fails to learn any schemata.


As a result of the extreme sparsity, this approach fails to learn any schemata.

we propose a more successful back-off strategy for higher-order RSI in the next section.


A requisite for a unary relation is that in the training KB there should exist many triples that share a relation and a particular entity as one argument, thus providing enough training for each unary classifier.

in the example above, we will not likely be able to generate predicates for all the 195 countries, because some of them will either not occur at all in the training data or they will be very infrequent.


we needed to create a new web-scale knowledge base population benchmark that we called CC-DBP 4 .


accurate -a strong assumption, especially for arbitrary documents from the Web.

there is a need to automatically predict the date of a document based on its content.


At k th layer, we compute gating value for a particular edge (u, v, l(u, v)) as: where, σ(•) is the sigmoid function,ŵ k l(u,v) and b k l(u,v) are label specific gating parameters.

gating helps to make the model robust to the noisy labels and directions of the input graphs.


Using the above state transition mechanism, information from each node propagates to all its neighboring nodes after each step.

for the worst case where the input graph is a chain of nodes, the maximum number of steps necessary for information from one arbitrary node to reach another is equal to the size of the graph.


Intuitively, θ t is relevant to the current decoder input e t and state s t , and the context vector µ t .

we define it as: where vectors w µ , w s , w e and scalar b 5 are model parameters.


We find that using adaptive learning rates for the optimization is efficient and leads the model to converge faster.

we use Adam (Kingma and Ba, 2015) with a learning rate of 0.0002 instead of stochastic gradient descent.


We compute the entailment score of S last (y) against all preceding sentences in x and y, and use the score of the sentence-pair for which we have the least confidence in a neutral classification: (6) Intuitively, we only use complete sentences because the ending of a sentence can easily flip entailment.

we carry over entailment score of the last complete sentence in a generation until the end of the next sentence, in order to maintain the presence of the entailment score in the objective.


"had a breakfast", "and a delicious breakfast").

sEQGAN quickly devolves into generic language, repeating the incredibly common sentence "The staff was very helpful and friendly.


As the pun data set is limited, the pun language model has no creativity, which means if we input a word appearing in the training data, then the output will usually be an existing sentence from the training data.

we remove the sentences which contain words in the 100 target words from the pun data set, and then train the model for test.


Here E() represents the embedding matrix as described earlier and BiLST M * represents a sequential application of the BiLST M function.

if there a total of m feature keys and embedding dimension is d, E(f move , f threat , f score ) is matrix of m * d. If hidden size of BILSTM is of size x, then g enc is of dimensionality m * x.


The commentary text corresponding to N M CR is selected as the output.

we need to consider a scoring function to find the closest matching data point in training set.


(2)), one needs to sample from the exponentiated pay-off distribution, which is quite challenging in practice.

importance sampling is often used (Norouzi et al., 2016;Ma et al., 2017).


Since the emission of eos ends the generation, the future pay-off can only come from the immediate increase of the pay-off.

we require Q R to be the incremental pay-off when producing eos, i.e.


In contrast, the model distribution P θ can only depend on x * .

the function approximator trained to recover Q * can take y * as input, making the estimation task much easier.


In contrast, minimum risk training (Shen et al., 2015) directly optimizes sentence-level BLEU.

it requires a large number (100) of samples per data to work well.


We suspect this is due to the multi-reference nature of the MSCOCO dataset, where a larger entropy is preferred.

the explicit entropy regularization in ERAC becomes immediately fruitful.


we obtain the final lexicon embeddings for the tokens in Q as a matrix E q ∈ R d×m and tokens in P as E p ∈ R d×n .


For practical deployment scenarios, prediction speed at test time is an important criterion.

one question is whether SAN can train with, e.g.


Our model as shown in Figure 2 is developed to generate answer from a single passage.

we need to extend it to handle multiple passages.


It requires less reasoning than previously thought and no need to comprehend the whole passage .

stanford publish a new large-scale dataset sQuAD (Rajpurkar et al., 2016), in which all the question and answers are manually created through crowdsourcing.


For the char embedding model, the ELMo language model (Peters et al., 2018) is used due to its superior performance in a wide range of NLP tasks.

we obtain two types of encoding vectors, i.e.


Besides, we think that the original representation and the aligned representation via attention can reflect the content semantics in different granularities.

we also apply a particular fusion function after each attention function, so that different levels of semantics can be better incorporated towards a better understanding.


Since we find that the original contextual representations are important in reflecting the semantics at a more global level, we also introduce different levels of gating mechanism to incorporate the projected representations m(•, •) with the original contextual representations.

the final fused representations of passage and question can be formulated as: where g (•, •) is a gating function.


Taking K as 2 for an example, we denote each probability shown on the right side of Equation 1 through sampling without replacement: (2) where we neglect Q, P i to abbreviate the conditional distributions in Equation 1.

the basic block of our candidate extraction stage turns out to be the distribution of each candidate P (C ij |Q, P i ).


After observing the TriviaQA dataset, we find that in this dataset only one or two retrieved paragraphs actually contain the correct answer.

simply using all retrieved paragraphs equally to extract answer may bring in much noise.


We can see from the statistics that the question body is much lengthier than the question subject.

it is necessary to condense the question representation.


To make it worse, its impact is magnified by the macroaveraged F1.

we adopt the F1 score and accuracy on two categories for evaluation.


SemEval 2017 regards answer selection as a ranking task, which is closer to the application scenario.

mean average precision (MAP) is used as an evaluation measure.


In order to mitigate the class imbalance problem, median frequency balancing Eigen and Fergus (2015) is used to reweight each class in the cross-entropy loss.

the rarer a class is in the training set, the larger weight it will get in the cross entropy loss.


QCN outperforms (4) by a great margin, demonstrating that subject-body relationship in community questions helps to condense question representation.

qCN can identify the meaningful part of the question representation that helps to determine answer quality.


P (y|x; θ) is defined on a holistic neural network which mainly includes two core components: an encoder encodes a source sentence x into a sequence of hidden representations H x = H 1 , ..., H M , and a decoder generates the n-th target word based on the sequence of hidden representations: where s n is the n-th hidden state on target side.

the model parameters of NMT include the parameter sets of the encoder θ enc and the decoder The standard training objective is to minimize the negative log-likelihood of the training corpus Due to the vulnerability and instability of deep neural networks, NMT models usually suffer from a drawback: small perturbations in the input can dramatically deteriorate its translation results.


Furthermore, the more target words are generated, the more time the self-attention in the decoder will take to model dependencies.

preserving the training efficiency of the Transformer on the one hand and accelerating its decoding on the other hand becomes a new and serious challenge.


Discriminative re-ranking: The above model is a locally-normalized model that provides a distribution for every decoded token, and thus might suffer from the label bias problem (Andor et al., 2016;Lafferty et al., 2001).

we add a globally-normalized re-ranker p ψ (z | x) that scores all |B| programs in the final beam produced by p θ (z | x).


2 If a program is spurious it is likely that it will yield the wrong denotation in one of those 4 images.

we can re-define each training example to be (x, {(k j , y j )} 4 j=1 ), where each utterance x is paired with 4 different KBs and the denotations of the utterance with respect to these KBs.


6), due to the difficulties of search and spuriousness.

we turn to learning from abstract examples, which substantially reduce these problems.


5.1, many utterances are identical at the abstract level.

a natural idea is to keep track at training time of abstract utteranceprogram pairs that resulted in a correct denotation, and use this information to direct the search procedure.


Assigning rewards at token-level will ease the feedback task and allow the semantic parser to learn from partially correct queries.

assuming the underlying policy can decompose over tokens, a token level (DPM+T) reward objective can be defined: Analogously, we can define an objective that combines the token-level rewards and the minibatched reweighting (DPM+T+OSL): Gradients for the DPM+T and DPM+T+OSL objectives are given in Table 1.


The automatic extension, obviating the need for expensive manual work, allows a vast increase of question-query pairs by an order of magnitude.

the number of tokens and types increase in a similar vein.


The requirement that t must be well-typed is crucial to ensure that it can be evaluated to an AMR graph, but as we show in the Supplementary Materials (Section A), makes the decoding problem NP-complete.


The supertag for VP coordination (with type [OP1[S], OP2[S]]) makes up for 0.4% of the training data, but 74% of its occurrences are recognized correctly (92% in 4-best).

the prediction of informative types for individual words is feasible.


This leads to AM dependency trees which are not well-typed for 75% of the sentences (we fall back to the largest welltyped subtree in these cases).

an off-theshelf dependency parser can reliably predict the tree structure of the AM dependency tree, but correct supertag and edge label assignment requires a decoder which takes the types into account.


With all terms pre-calculated before each iteration, the time complexity of computingL A is just O(k 2 ).

the total time complexity of computing L A is decreased to O(2|V |k 2 + 2|V |k + k 2 ) ≈ O(2|V |k 2 ), which is much smaller than the original O(k|V | 2 ).


In early experiments we observe that for both tasks, projecting with respect to all constraints of their original formulations using a generic quadratic program solver was prohibitively slow.

we construct relaxed polytopes by considering only a subset of the constraints.


In comparison, high-quality crowdsourced datasets are much smaller in size, and the annotation process is quite expensive because the labeled examples require expertise and careful design (Chen et al., 2016).

there is a need for methods that can automatically generate high-quality question-answer pairs.


For example, in Figure 1, the second and third questions require coreference information (i.e., recognizing that "His" in sentence 2 and "He" in sentence 3 both corefer with "Tesla" in sentence 1) to answer them.

our research studies methods for incorporating coreference information into the training of a question generation system.


However, this success on single Wikipedia passage is still not adequate, considering the ultimate goal of reading the whole web.

several latest datasets (Nguyen et al., 2016;He et al., 2017;Dunn et al., 2017) attempt to design the MRC tasks in more realistic settings by involving search engines.


As is shown by Jia and Liang (2017), these confusing answer candidates could be quite difficult for MRC models to distinguish.

special consideration is required for such multi-passage MRC problem.


The mixed economy is a balance between socialism and capitalism.

some institutions are owned and maintained by .


According to our design, these three tasks can share the same embedding, encoding and matching layers.

we propose to train them together as multi-task learning (Ruder, 2017).


(2017) argues that the boundary loss encourages exact answers at the cost of penalizing overlapping answers.

they propose a mixed objective that incorporates rewards derived from word overlap.


Then we merge continuous spans in s and assign the remaining spans in s to the incoming red dashed edge of n. We apply a similar method to the derivation tree.

every inner node of the derivation tree is associated with a span.


We can produce new rules by removing or duplicating more of them.

for example: we get the two rules below: During decoding, when neither induced nor extended rule is applicable, we create a dynamic rule on-the-fly.


3 In many sequence models where the vocabulary size is large, exact inference by finding the true shortest path in the graph discussed in Section 3.2 is intractable.

approximate inference techniques such as beam search are often used, or the size of the search space is reduced, for example, by using a Markov assumption.


We provide an aligned sample for illustration in Figure 2.

every single unit in a sample is a trading day, and we can predict a sequence of movements y = [y 1 , .


We observe the following factorization, Neural approximation aims at minimizing the Kullback-Leibler divergence between the q φ (Z|X, y) and p θ (Z|X, y).

instead of optimizing it directly, we observe that the following equation naturally holds, where D KL [q p] is the Kullback-Leibler divergence between the distributions q and p. we equivalently maximize the following variational recurrent lower bound by plugging Eq.


We integrate the two and acquire the final normalized attention weight v * ∈ R 1×(T −1) by feeding their elementwise product into the softmax function.

the main prediction can benefit from temporally-close hypotheses have been made and we decode our main hypothesisỹ T as where W T is a weight matrix and b T is a bias.


Since y * also involves in generating y T through the temporal attention, tweaking α acts as a tradeoff between focusing on the main target and generalizing by denoising.

as shown in Figure 4, our models do not linearly benefit from incorporating temporal auxiliary.